How to delete conda environment

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Rearrange a linked list such that all even index nodes come after all odd index nodes

Problem Statement

Given a singly linked list, group all odd nodes together followed by all the even nodes. Please note here we are talking about the node number and not the value in the nodes.

The program should run in O(1) space complexity and O(nodes) time complexity.

VIF implementation in python

Variance Inflation Factor(or VIF) is a technique to detect the multicollinearity among the input variables. Multicollinearity occurs when independent variables in a regression model are correlated. This is a problem because it violates the fundamental assumption in a regression model – There should be minimum or no correlation among the independent variables.

Multicollinearity usually doesn’t affect the predictive power of the model but, it does impact the interpretability.

Delete all docker images at once(command explained)

To remove all the images at once, the command is,

Let’s break the command into two parts –

Check multiple column existence in a Pandas DataFrame

To check if one or more column all exist in a Pandas DataFrame

Method 1 – set.subset()

To check if one or more columns all exist in a Dataframe, we can leverage set() data structure in python and its issubset() method something like this –

Delete all docker containers at once(command explained)

To remove all the containers at once the command is,

Let’s break the command into two parts –

Explanation –

Delete Conda Environment (7 commands)

In this article, we have explained and presented 7 commands to delete a Conda environment permanently. We can delete a conda environment either by name or by path.

Table of contents:

Step 1: Find the Conda environment to delete

To find the name of the environment you want to delete, we can get the list of all Conda environments as follows:

Step 2: Get out of the environment

You cannot delete the conda environment you are within. To get out of the current environment, use the command:

Step 3: Delete the Conda Environment (6 commands)

Alternatively, we can use the following command:

If you have the path where a conda environment is located, you can directly specify the path instead of name of the conda environment. In this case, the command will be:

Delete Directory directly?

It is not advised to delete the directory directly where the conda environment is stored. In some cases, it might be necessary so the steps are:

Why to delete a Conda environment?

You may need to delete a conda environment for the following reasons:

With this article at OpenGenus, you must have the complete idea of how to delete a conda environment.

Ue Kiao, PhD

Ue Kiao is a Technical Author and Software Developer with B. Sc in Computer Science at National Taiwan University and PhD in Algorithms at Tokyo Institute of Technology | Researcher at TaoBao

Managing environmentsпѓЃ

With conda, you can create, export, list, remove, and update environments that have different versions of Python and/or packages installed in them. Switching or moving between environments is called activating the environment. You can also share an environment file.

conda activate and conda deactivate only work on conda 4.6 and later versions. For conda versions prior to 4.6, run:

Windows: activate or deactivate

Linux and macOS: source activate or source deactivate

Creating an environment with commandsпѓЃ

Use the terminal or an Anaconda Prompt for the following steps:

To create an environment:

Replace myenv with the environment name.

When conda asks you to proceed, type y :

To create an environment with a specific version of Python:

To create an environment with a specific package:

To create an environment with a specific version of a package:

To create an environment with a specific version of Python and multiple packages:

Install all the programs that you want in this environment at the same time. Installing 1 program at a time can lead to dependency conflicts.

Creating an environment from an environment.yml fileпѓЃ

Use the terminal or an Anaconda Prompt for the following steps:

Create the environment from the environment.yml file:

Activate the new environment: conda activate myenv

Verify that the new environment was installed correctly:

Specifying a location for an environmentпѓЃ

You can control where a conda environment lives by providing a path to a target directory when creating the environment. For example, the following command will create a new environment in a subdirectory of the current working directory called envs :

You then activate an environment created with a prefix using the same command used to activate environments created by name:

Specifying a path to a subdirectory of your project directory when creating an environment has the following benefits:

It makes it easy to tell if your project uses an isolated environment by including the environment as a subdirectory.

It makes your project more self-contained as everything, including the required software, is contained in a single project directory.

An additional benefit of creating your project’s environment inside a subdirectory is that you can then use the same name for all your environments. If you keep all of your environments in your envs folder, you’ll have to give each environment a different name.

There are a few things to be aware of when placing conda environments outside of the default envs folder.

Specifying an install path when creating your conda environments makes it so that your command prompt is now prefixed with the active environment’s absolute path rather than the environment’s name.

After activating an environment using its prefix, your prompt will look similar to the following:

This can result in long prefixes:

Now your command prompt will display the active environment’s generic name, which is the name of the environment’s root folder:

Updating an environmentпѓЃ

You may need to update your environment for a variety of reasons. For example, it may be the case that:

one of your core dependencies just released a new version (dependency version number update).

you need an additional package for data analysis (add a new dependency).

you have found a better package and no longer need the older package (add new dependency and remove old dependency).

If any of these occur, all you need to do is update the contents of your environment.yml file accordingly and then run the following command:

Cloning an environmentпѓЃ

Use the terminal or an Anaconda Prompt for the following steps:

You can make an exact copy of an environment by creating a clone of it:

Replace myclone with the name of the new environment. Replace myenv with the name of the existing environment that you want to copy.

To verify that the copy was made:

In the environments list that displays, you should see both the source environment and the new copy.

Building identical conda environmentsпѓЃ

You can use explicit specification files to build an identical conda environment on the same operating system platform, either on the same machine or on a different machine.

Use the terminal or an Anaconda Prompt for the following steps:

To create this spec list as a file in the current working directory, run:

You can use spec-file.txt as the filename or replace it with a filename of your choice.

An explicit spec file is not usually cross platform, and therefore has a comment at the top such as # platform: osx-64 showing the platform where it was created. This platform is the one where this spec file is known to work. On other platforms, the packages specified might not be available or dependencies might be missing for some of the key packages already in the spec.

To use the spec file to create an identical environment on the same machine or another machine:

To use the spec file to install its listed packages into an existing environment:

Conda does not check architecture or dependencies when installing from a spec file. To ensure that the packages work correctly, make sure that the file was created from a working environment, and use it on the same architecture, operating system, and platform, such as linux-64 or osx-64.

Activating an environmentпѓЃ

When installing Anaconda, you have the option to “Add Anaconda to my PATH environment variable.” This is not recommended because the add to PATH option appends Anaconda to PATH. When the installer appends to PATH, it does not call the activation scripts.

On Windows, PATH is composed of two parts, the system PATH and the user PATH. The system PATH always comes first. When you install Anaconda for Just Me, we add it to the user PATH. When you install for All Users, we add it to the system PATH. In the former case, you can end up with system PATH values taking precedence over our entries. In the latter case, you do not. We do not recommend multi-user installs.

Activation prepends to PATH. This only takes effect when you have the environment active so it is local to a terminal session, not global.

To activate an environment: conda activate myenv

Replace myenv with the environment name or directory path.

Conda prepends the path name myenv onto your system command.

You may receive a warning message if you have not activated your environment:

If you receive this warning, you need to activate your environment. To do so on Windows, run: c:\Anaconda3\Scripts\activate base in Anaconda Prompt.

Windows is extremely sensitive to proper activation. This is because the Windows library loader does not support the concept of libraries and executables that know where to search for their dependencies (RPATH). Instead, Windows relies on a dynamic-link library search order.

If environments are not active, libraries won’t be found and there will be lots of errors. HTTP or SSL errors are common errors when the Python in a child environment can’t find the necessary OpenSSL library.

Conda itself includes some special workarounds to add its necessary PATH entries. This makes it so that it can be called without activation or with any child environment active. In general, calling any executable in an environment without first activating that environment will likely not work. For the ability to run executables in activated environments, you may be interested in the conda run command.

Conda initпѓЃ

One setting may be useful to you when using conda init is:

Nested activationпѓЃ

By default, conda activate will deactivate the current environment before activating the new environment and reactivate it when deactivating the new environment. Sometimes you may want to leave the current environment PATH entries in place so that you can continue to easily access command-line programs from the first environment. This is most commonly encountered when common command-line utilities are installed in the base environment. To retain the current environment in the PATH, you can activate the new environment using:

If you wish to always stack when going from the outermost environment, which is typically the base environment, you can set the auto_stack configuration option:

You may specify a larger number for a deeper level of automatic stacking, but this is not recommended since deeper levels of stacking are more likely to lead to confusion.

Environment variable for DLL loading verificationпѓЃ

If you choose not to activate your environment, then loading and setting environment variables to activate scripts will not happen. We only support activation.

Deactivating an environmentпѓЃ

To deactivate an environment, type: conda deactivate

Conda removes the path name for the currently active environment from your system command.

Determining your current environmentпѓЃ

Use the terminal or an Anaconda Prompt for the following steps.

By default, the active environment—the one you are currently using—is shown in parentheses () or brackets [] at the beginning of your command prompt:

If you do not see this, run:

In the environments list that displays, your current environment is highlighted with an asterisk (*).

By default, the command prompt is set to show the name of the active environment. To disable this option:

To re-enable this option:

Viewing a list of your environmentsпѓЃ

To see a list of all of your environments, in your terminal window or an Anaconda Prompt, run:

A list similar to the following is displayed:

If this command is run by an administrator, a list of all environments belonging to all users will be displayed.

Viewing a list of the packages in an environmentпѓЃ

To see a list of all packages installed in a specific environment:

If the environment is not activated, in your terminal window or an Anaconda Prompt, run:

If the environment is activated, in your terminal window or an Anaconda Prompt, run:

To see if a specific package is installed in an environment, in your terminal window or an Anaconda Prompt, run:

Using pip in an environmentпѓЃ

To use pip in your environment, in your terminal window or an Anaconda Prompt, run:

Issues may arise when using pip and conda together. When combining conda and pip, it is best to use an isolated conda environment. Only after conda has been used to install as many packages as possible should pip be used to install any remaining software. If modifications are needed to the environment, it is best to create a new environment rather than running conda after pip. When appropriate, conda and pip requirements should be stored in text files.

We recommend that you:

Use pip only after conda

Install as many requirements as possible with conda then use pip.

Create a conda environment to isolate any changes pip makes.

Environments take up little space thanks to hard links.

Care should be taken to avoid running pip in the root environment.

Once pip has been used, conda will be unaware of the changes.

To install additional conda packages, it is best to recreate the environment.

Conda env will export or create environments based on a file with conda and pip requirements.

Setting environment variablesпѓЃ

If you want to associate environment variables with an environment, you can use the config API. This is recommended as an alternative to using activate and deactivate scripts since those are an execution of arbitrary code that may not be safe.

First, create your environment and activate it:

When you deactivate your environment, you can use those same commands to see that the environment variable goes away.

When you deactivate your environment, you can see that environment variable goes away by rerunning echo my_var or conda env config vars list to show that the variable name is no longer present.

Saving environment variablesпѓЃ

Conda environments can include saved environment variables.

This type of script file can be part of a conda package, in which case these environment variables become active when an environment containing that package is activated.

WindowsпѓЃ

Enter that directory and create these subdirectories and files:

macOS and LinuxпѓЃ

Enter that directory and create these subdirectories and files:

Sharing an environmentпѓЃ

You may want to share your environment with someone else—for example, so they can re-create a test that you have done. To allow them to quickly reproduce your environment, with all of its packages and versions, give them a copy of your environment.yml file.

Exporting the environment.yml fileпѓЃ

If you already have an environment.yml file in your current directory, it will be overwritten during this task.

Activate the environment to export: conda activate myenv

Replace myenv with the name of the environment.

Export your active environment to a new file:

This file handles both the environment’s pip packages and conda packages.

Email or copy the exported environment.yml file to the other person.

Exporting an environment file across platformsпѓЃ

For example, if you create an environment and install Python and a package:

This will download and install numerous additional packages to solve for dependencies. This will introduce packages that may not be compatible across platforms.

Creating an environment file manuallyпѓЃ

You can create an environment file ( environment.yml ) manually to share with others.

EXAMPLE: A simple environment file:

EXAMPLE: A more complex environment file:

Note the use of the wildcard * when defining the patch version number. Defining the version number by fixing the major and minor version numbers while allowing the patch version number to vary allows us to use our environment file to update our environment to get any bug fixes whilst still maintaining consistency of software environment.

You can exclude the default channels by adding nodefaults to the channels list.

Restoring an environmentпѓЃ

Replace REVNUM with the revision number.

Removing an environmentпѓЃ

To remove an environment, in your terminal window or an Anaconda Prompt, run:

To verify that the environment was removed, in your terminal window or an Anaconda Prompt, run:

The environments list that displays should not show the removed environment.

Working with Environments

Overview

What is a Conda environment?

How do I create (delete) an environment?

How do I activate (deactivate) an environment?

How do I install packages into existing environments using Conda (+pip)?

Where should I create my environments?

How do I find out what packages have been installed in an environment?

How do I find out what environments that exist on my machine?

How do I delete an environment that I no longer need?

Understand how Conda environments can improve your research workflow.

Create a new environment.

Activate (deactivate) a particular environment.

Install packages into existing environments using Conda (+pip).

Specify the installation location of an environment.

List all of the existing environments on your machine.

List all of the installed packages within a particular environment.

Delete an entire environment.

Workspace for Conda environments

If you haven’t done it yet, create a new introduction-to-conda-for-data-scientists directory on your Desktop in order to maintain a consistent workspace for all your conda environment.

On Mac OSX and Linux running following commands in the Terminal will create the required directory on the Desktop.

For Windows users you may need to reverse the direction of the slash and run the commands from the command prompt.

Alternatively, you can always “right-click” and “create new folder” on your Desktop. All the commands that are run during the workshop should be run in a terminal within the introduction-to-conda-for-data-scientists directory.

What is a Conda environment

A Conda environment is a directory that contains a specific collection of Conda packages that you have installed. For example, you may be working on a research project that requires NumPy 1.18 and its dependencies, while another environment associated with an finished project has NumPy 1.12 (perhaps because version 1.12 was the most current version of NumPy at the time the project finished). If you change one environment, your other environments are not affected. You can easily activate or deactivate environments, which is how you switch between them.

Avoid installing packages into your base Conda environment

Conda has a default environment called base that include a Python installation and some core system libraries and dependencies of Conda. It is a “best practice” to avoid installing additional packages into your base software environment. Additional packages needed for a new project should always be installed into a newly created Conda environment.

Creating environments

To create a new environment for Python development using conda you can use the conda create command.

For a list of all commands, take a look at Conda general commands.

The command above will create a new Conda environment called “python3” and install the most recent version of Python. If you wish, you can specify a particular version of packages for conda to install when creating the environment.

Always specify a version number for each package you wish to install

In order to make your results more reproducible and to make it easier for research colleagues to recreate your Conda environments on their machines it is a “best practice” to always explicitly specify the version number for each package that you install into an environment. If you are not sure exactly which version of a package you want to use, then you can use search to see what versions are available using the conda search command.

So, for example, if you wanted to see which versions of Scikit-learn, a popular Python library for machine learning, were available, you would run the following.

You can create a Conda environment and install multiple packages by listing the packages that you wish to install.

When conda installs a package into an environment it also installs any required dependencies. For example, even though Python is not listed as a packaged to install into the basic-scipy-env environment above, conda will still install Python into the environment because it is a required dependency of at least one of the listed packages.

Creating a new environment

Create a new environment called “machine-learning-env” with Python and the most current versions of IPython, Matplotlib, Pandas, Numba and Scikit-Learn.

Solution

In order to create a new environment you use the conda create command as follows.

Since no version numbers are provided for any of the Python packages, Conda will download the most current, mutually compatible versions of the requested packages. However, since it is best practice to always provide explicit version numbers, you should prefer the following solution.

However, please be aware that the version numbers for each packages may not be the latest available and would need to be adjusted.

Activating an existing environment

Activating environments is essential to making the software in environments work well (or sometimes at all!). Activation of an environment does two things.

Step 2 is particularly important as activation scripts are how packages can set arbitrary environment variables that may be necessary for their operation. Aou activate the basic-scipy-env environment by name using the activate command.

You can see that an environment has been activated because the shell prompt will now include the name of the active environment.

Deactivate the current environment

To deactivate the currently active environment use the Conda deactivate command as follows.

You can see that an environment has been deactivated because the shell prompt will no longer include the name of the previously active environment.

Returning to the base environment

Activate an existing environment by name

Activate the machine-learning-env environment created in the previous challenge by name.

Solution

In order to activate an existing environment by name you use the conda activate command as follows.

Deactivate the active environment

Deactivate the machine-learning-env environment that you activated in the previous challenge.

Solution

In order to deactivate the active environment you use the conda deactivate command.

Installing a package into an existing environment

You can install a package into an existing environment using the conda install command. This command accepts a list of package specifications (i.e., numpy=1.18 ) and installs a set of packages consistent with those specifications and compatible with the underlying environment. If full compatibility cannot be assured, an error is reported and the environment is not changed.

By default the conda install command will install packages into the current, active environment. The following would activate the basic-scipy-env we created above and install Numba, an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code, into the active environment.

As was the case when listing packages to install when using the conda create command, if version numbers are not explicitly provided, Conda will attempt to install the newest versions of any requested packages. To accomplish this, Conda may need to update some packages that are already installed or install additional packages. It is always a good idea to explicitly provide version numbers when installing packages with the conda install command. For example, the following would install a particular version of Scikit-Learn, into the current, active environment.

Freezing installed packages

Installing a package into a specific environment

Dask provides advanced parallelism for data science workflows enabling performance at scale for the core Python data science tools such as Numpy Pandas, and Scikit-Learn. Have a read through the official documentation for the conda install command and see if you can figure out how to install Dask into the machine-learning-env that you created in the previous challenge.

Solution

You can install Dask into machine-learning-env using the conda install command as follow.

You could also install Dask into machine-learning-env by first activating that environment and then using the conda install command.

Where do Conda environments live?

Running ls (linux) / dir (Windows) on your anaconda envs/ directory will list out the directories containing the existing Conda environments.

Location of Conda environments on Binder

If you are working through these lessons using a Binder instance, then the default location of the Conda environments is slightly different.

Running ls /srv/conda/envs/ from a terminal will list out the directories containing any previously installed Conda environments.

How do I specify a location for a Conda environment?

You activate an environment created with a prefix using the same command used to activate environments created by name.

It is often a good idea to specify a path to a sub-directory of your project directory when creating an environment. Why?

An additional benefit of creating your project’s environment inside a sub-directory is that you can then use the same name for all your environments; if you keep all of your environments in your

/miniconda3/env/ folder, you’ll have to give each of them a different name.

Conda environment sub-directory naming convention

Whatever naming convention you adopt it is important to be consistent! Using the same name for all of your Conda environments allows you to use the same activate command as well.

Creating a new environment as a sub-directory within a project directory

First create a project directory called project-dir using the following command.

Next, create a new environment inside the newly created project-dir in a sub-directory called env an install Python 3.6, version 3.1 of Matplotlib, and version 2.0 of TensorFlow.

Solution

Placing Conda environments outside of the default

Second, an annoying side-effect of specifying an install path when creating your Conda environments is that your command prompt is now prefixed with the active environment’s absolute path rather than the environment’s name. After activating an environment using its prefix your prompt will look similar to the following.

As you can imagine, this can quickly get out of hand.

This will either edit your

/.condarc file if you already have one or create a

/.condarc file if you do not. Now your command prompt will display the active environment’s generic name.

Activate an existing environment by path

Activate the environment created in a previous challenge using the path to the environment directory.

Solution

You can activate an existing environment by providing the path the the environment directory instead of the environment name when using the conda activate command as follows.

Conda can create environments for R projects too!

First create a project directory called r-project-dir using the following command.

Solution

Listing existing environments

Now that you have created a number of Conda environments on your local machine you have probably forgotten the names of all of the environments and exactly where they live. Fortunately, there is a conda command to list all of your existing environments together with their locations.

Listing the contents of an environment

In addition to forgetting names and locations of Conda environments, at some point you will probably forget exactly what has been installed in a particular Conda environment. Again, there is a conda command for listing the contents on an environment. To list the contents of the basic-scipy-env that you created above, run the following command.

Listing the contents of a particular environment.

List the packages installed in the machine-learning-env environment that you created in a previous challenge.

Solution

You can list the packages and their versions installed in machine-learning-env using the conda list command as follows.

Deleting entire environments

Occasionally, you will want to delete an entire environment. Perhaps you were experimenting with conda commands and you created an environment you have no intention of using; perhaps you no longer need an existing environment and just want to get rid of cruft on your machine. Whatever the reason, the command to delete an environment is the following.

Delete an entire environment

Delete the entire “basic-scipy-env” environment.

Solution

In order to delete an entire environment you use the conda remove command as follows.

Key Points

A Conda environment is a directory that contains a specific collection of Conda packages that you have installed.

You create (remove) a new environment using the conda create ( conda remove ) commands.

You activate (deactivate) an environment using the conda activate ( conda deactivate ) commands.

You should install each environment as a sub-directory inside its corresponding project directory

Use the conda env list command to list existing environments and their respective locations.

Use the conda list command to list all of the packages installed in an environment.

Manage your Python Virtual Environment with Conda

Toggle between your Python 2 and Python 3 environments with ease

Although Python 2 is officially deprecated (Python 3, your time is now!), I believe some of us still have to maintain existing Python 2 projects before fully port those projects to Python 3.

In this article, I will show you how to manage your Python virtual environment by using Conda. Conda is a package and environment management system, it allows us to create and switch environments easily on our local machine.

Using Conda Environment

I will be using the iTerm terminal in macOS. Windows user will have to use Anaconda Prompt if you never add the Conda environment path to your windows environment variable.

1. Display Conda environment information

To show information about current Conda install.

2. List all existing environments

We can use conda env list to list all existing Python environments. The * will point to the current active environment. base is always the default active environment when you open your terminal. py2 is my another virtual environment for my Python 2 projects.

3. Create a new environment

After creating your new environment, you can run conda env list to check your new environment.

4. Activate your new environment

As you can see, you have successfully create your new Python environment. Now we have to activate and use it.

The (project-env) indicates the current active environment.

You can use conda list to display all packages in this environment.

conda list also supports revision history. It is an incredibly useful feature to have to manage your package in the environment.

Imagine you accidentally screw up your dependencies just because you update/install a new package. Revision history allows you to rollback your environment to the previous version just like that.

configparser has been removed successfully. You can use conda list — revision to check again.

4. Deactivate/Change your active environment

conda deactivate will deactivate your current active environment and change to the default environment which is the base environment.

5. Remove your environment

While you are done with this environment and wish to remove it. You can use conda env remove to remove the environment.

The project-env has been successfully removed from your environments.

6. Bonus! Change your environment in VS Code.

If you are a Visual Studio Code fans like me, you can change your Python environment that you created without any command line.

Now you have learnt how to manage your Python environment with Conda. Do drop me a comment if I made any mistake or typo. Cheers!

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